HW [Group Assignment]

Each group member must uniquely complete this assignment for the group to get credit for this homework. If only some members of the group complete the assignment, they must each submit a supplemental explanation along with their submission as to why the other students in their group have not completed this assignment.

Review the the ggplot2 documentation and related resources to your own satisfaction and as needed to create the exercise
Find a data set you’re interested in that lends itself to this assignment
- As noted in Pirates_Snakes.Rmd and Pirates_Snakes.ipynb (from Class 5) and in the code below (Class 6) some ways to find data are
  - data analysis websites such as kaggle data repositories such as those provided by seaborn and CMU (e.g., we’ve used the penguins, strikes, and S&P) data sets)
  - other similar resources, such as (in no particular order)
- data() will show you data sets available in R
  - And you can load data as done in Pirates_Snakes.Rmd (Class 6), e.g., data("USArrests")
    - HOWEVER, if you use a built in data set, the maximum grade you can get on this assignment is 90%, with the following exception:
      - 10% of the assignment is for “reading in” data from a .csv or other tabular “non built-in” data source
      - …so you you cat get full 100% by first saving an internal data set to a .csv or other tabular data format and THEN reading it into R (to get credit for the last 10%
Create an attractive graphical image presenting information in the data
Use both the size and color attributes in your graphic
- These aesthetics are demonstrated in the “hwy versus displ” plots here:
  - ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class)) # (colored by class)
  - ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, size = class))
    # (sized by class)
Use the facets feature of ggplot2
- This aesthetic is demonstrated here
For +10 points, use a “color blind friendly” color palette.
- One example of how to do this with scale_colour_manual(values=cbbPalette) and cbbPalette <- c("#009E73", "#CC79A7","#D55E00", "#000000","#56B4E9", "#F0E442", "#0072B2", "#E69F00") is described here

Hint: you’ll need 3 factor variables and two continuous variables to do this, though continuous variables can be discretized to produce factor level variables.

ggplot2 (R)

Perhaps the best (most principled) plotting library around
Here are the references/resources I’ve used to create the code in this file

Canonical Resources:

Manual Pages

Additional Resources

Data Sets

Code

Initialization

loading tidyverse, data, and joining tables

library(tidyverse)

# https://readr.tidyverse.org/reference/read_delim.html
d1 <- "/Users/gck8gd/Documents/courses/SDS_3003_Communication_w_Data/primary_results.csv"
primary_results <- readr::read_csv(d1)
d2 <- "/Users/gck8gd/Documents/courses/SDS_3003_Communication_w_Data/county_facts.csv"
county_facts <- readr::read_csv(d2)

# https://stringr.tidyverse.org/reference/str_replace.html
# https://dplyr.tidyverse.org/reference/join.html
county_facts %>% dplyr::mutate(county=stringr::str_replace(area_name, 
                                                          ' County', '')) %>%
  dplyr::inner_join(primary_results, by=c('state_abbreviation','county')) ->
  county_factsANDprimaries

Elements of a ggplot

`ggplot` function

my_first_ggplot <- ggplot(data=county_factsANDprimaries)

my_first_ggplot

`ggplot` parts

ls(my_first_ggplot)

## [1] "coordinates" "data"        "facet"       "labels"      "layers"     
## [6] "mapping"     "plot_env"    "scales"      "theme"

`ggplot` data

my_first_ggplot[['data']]

## # A tibble: 17,028 x 61
##    fips.x area_name state_abbreviat… PST045214 PST040210 PST120214 POP010210
##     <dbl> <chr>     <chr>                <dbl>     <dbl>     <dbl>     <dbl>
##  1   1001 Autauga … AL                   55395     54571       1.5     54571
##  2   1001 Autauga … AL                   55395     54571       1.5     54571
##  3   1001 Autauga … AL                   55395     54571       1.5     54571
##  4   1001 Autauga … AL                   55395     54571       1.5     54571
##  5   1001 Autauga … AL                   55395     54571       1.5     54571
##  6   1001 Autauga … AL                   55395     54571       1.5     54571
##  7   1001 Autauga … AL                   55395     54571       1.5     54571
##  8   1003 Baldwin … AL                  200111    182265       9.8    182265
##  9   1003 Baldwin … AL                  200111    182265       9.8    182265
## 10   1003 Baldwin … AL                  200111    182265       9.8    182265
## # … with 17,018 more rows, and 54 more variables: AGE135214 <dbl>,
## #   AGE295214 <dbl>, AGE775214 <dbl>, SEX255214 <dbl>, RHI125214 <dbl>,
## #   RHI225214 <dbl>, RHI325214 <dbl>, RHI425214 <dbl>, RHI525214 <dbl>,
## #   RHI625214 <dbl>, RHI725214 <dbl>, RHI825214 <dbl>, POP715213 <dbl>,
## #   POP645213 <dbl>, POP815213 <dbl>, EDU635213 <dbl>, EDU685213 <dbl>,
## #   VET605213 <dbl>, LFE305213 <dbl>, HSG010214 <dbl>, HSG445213 <dbl>,
## #   HSG096213 <dbl>, HSG495213 <dbl>, HSD410213 <dbl>, HSD310213 <dbl>,
## #   INC910213 <dbl>, INC110213 <dbl>, PVY020213 <dbl>, BZA010213 <dbl>,
## #   BZA110213 <dbl>, BZA115213 <dbl>, NES010213 <dbl>, SBO001207 <dbl>,
## #   SBO315207 <dbl>, SBO115207 <dbl>, SBO215207 <dbl>, SBO515207 <dbl>,
## #   SBO415207 <dbl>, SBO015207 <dbl>, MAN450207 <dbl>, WTN220207 <dbl>,
## #   RTN130207 <dbl>, RTN131207 <dbl>, AFN120207 <dbl>, BPS030214 <dbl>,
## #   LND110210 <dbl>, POP060210 <dbl>, county <chr>, state <chr>, fips.y <dbl>,
## #   party <chr>, candidate <chr>, votes <dbl>, fraction_votes <dbl>

`aes` aesthetic mappings

my_first_ggplot[['mapping']]

## Aesthetic mapping: 
## <empty>

my_first_ggplot <- ggplot(data=county_factsANDprimaries,
                          mapping=aes(x=PST045214, y=fraction_votes))

my_first_ggplot[['mapping']]

## Aesthetic mapping: 
## * `x` -> `PST045214`
## * `y` -> `fraction_votes`

my_first_ggplot

`geom` rendering

my_first_ggplot + geom_point(mapping=aes(x=PST045214, y=fraction_votes)) -> 
  my_first_rendered_ggplot

my_first_rendered_ggplot

adjusting scales

show data on appropriate scales

# https://r4ds.had.co.nz/graphics-for-communication.html#scales
# https://r4ds.had.co.nz/graphics-for-communication.html#replacing-a-scale
my_first_rendered_ggplot + scale_x_log10()

rendering style type

rule of proportional ink
plotting etiquette: color vision deficiency

# https://stackoverflow.com/questions/7714677/scatterplot-with-too-many-points
my_first_ggplot + geom_point() + scale_x_log10()

my_first_ggplot + geom_point(alpha=0.1) + scale_x_log10()

my_first_ggplot + geom_point(alpha=0.1, size=2.5, stroke=0) + scale_x_log10()

my_first_ggplot + geom_hex() + scale_x_log10()

my_first_ggplot + scale_x_log10() + geom_density_2d()

my_first_ggplot + geom_hex() + geom_density_2d() + scale_x_log10()

# https://ggplot2.tidyverse.org/reference/scale_viridis.html
my_first_ggplot + geom_hex() + geom_density_2d() + scale_x_log10() +
  scale_fill_viridis_c()

my_first_ggplot + scale_fill_viridis_c() + scale_x_log10() +
  stat_density_2d(aes(fill=stat(density)), geom='raster', contour=FALSE) +
  coord_cartesian(expand=FALSE) + 
  geom_point(color='white', shape='.', size=0.0001, alpha=0.1)

more `aes` options

size
color
alpha

my_second_ggplot <- ggplot(data=county_factsANDprimaries,
                           mapping=aes(size=POP060210, alpha=POP060210,
                                       color=party, 
                                       x=PST045214, y=fraction_votes))

my_second_ggplot + geom_point() + scale_x_log10()

labels

you will need to do something like this for your homework

county_factsANDprimaries <- county_factsANDprimaries %>% 
  dplyr::rename(`County Population`=PST045214, 
                `County Density (per square mile)`=POP060210,
                Party=party, `Fraction of Votes`=fraction_votes) 


my_second_ggplot <- ggplot(data=county_factsANDprimaries,
                           mapping=aes(size=`County Density (per square mile)`, 
                                       alpha=`County Density (per square mile)`,
                                       color=Party, 
                                       x=`County Population`, 
                                       y=`Fraction of Votes`))

my_second_ggplot + geom_point() + scale_x_log10()

facets

this is exactly like what you will need to do for your homework
but critique what I’ve done here
- what do you like about it?
- what don’t you like about it?

county_factsANDprimaries <- county_factsANDprimaries %>% 
  dplyr::mutate(Candidate = stringr::str_replace(candidate, ' ','\n'))

  
my_third_ggplot <- ggplot(data=county_factsANDprimaries,
                          mapping=aes(size=`County Population`, 
                                      alpha=`County Population`,
                                      color=Party, 
                                      x=`County Density (per square mile)`, 
                                      y=`Fraction of Votes`))

my_third_ggplot + geom_point() + scale_x_log10() +
  facet_wrap(~ Candidate, nrow = 2)

touchups

# https://community.rstudio.com/t/x-axis-labels-overlap-want-to-rotate-labels-45/63800
# http://www.sthda.com/english/wiki/ggplot2-axis-ticks-a-guide-to-customize-tick-marks-and-labels
my_third_ggplot + geom_point() + scale_x_log10() +
  facet_wrap(~ Candidate, nrow=4, scales='fixed') + 
  scale_color_hue(direction=-1) +
  scale_y_continuous(breaks=c(0,0.2,0.4,0.6,0.8, 1.0),
                     guide=guide_axis(n.dodge=2, check.overlap=TRUE)) +
  coord_flip()

ggplot2_1

Schwartz

9/19/2020

HW [Group Assignment]

ggplot2 (R)

Canonical Resources:

Manual Pages

Additional Resources

Data Sets

Code

Initialization

Elements of a ggplot

`ggplot` function

`ggplot` parts

`ggplot` data

`aes` aesthetic mappings

`geom` rendering

adjusting scales

rendering style type

more `aes` options

labels

facets

touchups

ggplot2_1

Schwartz

9/19/2020

HW [Group Assignment]

ggplot2 (R)

Canonical Resources:

Manual Pages

Additional Resources

Data Sets

Code

Initialization

Elements of a ggplot

ggplot function

ggplot parts

ggplot data

aes aesthetic mappings

geom rendering

adjusting scales

rendering style type

more aes options

labels

facets

touchups

`ggplot` function

`ggplot` parts

`ggplot` data

`aes` aesthetic mappings

`geom` rendering

more `aes` options